{"id":37278,"date":"2023-09-14T10:51:11","date_gmt":"2023-09-14T09:51:11","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=37278"},"modified":"2023-09-14T10:51:11","modified_gmt":"2023-09-14T09:51:11","slug":"brain-injury-recovery-predicted-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/brain-injury-recovery-predicted-artificial-intelligence\/37278\/","title":{"rendered":"Brain injury recovery predicted with Artificial Intelligence"},"content":{"rendered":"

Two graduate students from Western University have developed a groundbreaking method for predicting brain injury recovery in ICU patients.<\/h2>\n

Matthew Kolisnyk and Karnig Kazazian, PhD candidates at the Schulich School of Medicine & Dentistry in the lab of neuroscientist Adrian Owen, combined functional magnetic resonance imaging (fMRI) with Machine Learning techniques to predict which patients will survive a severe brain injury.<\/p>\n

The study, \u2018Predicting neurologic recovery after severe acute brain injury using resting-state networks,<\/a>\u2019 is published in the Journal of Neurology. <\/em><\/p>\n

Uncertainty around brain injury recovery<\/h3>\n

Whether a traumatic brain injury is caused by a stroke or cardiac arrest, lives can be changed forever.<\/p>\n

When patients with serious brain injuries are admitted to the ICU, families are faced with great uncertainty around recovery.<\/p>\n

Equally uncertain about brain injury recovery, however, are the health professionals.<\/p>\n

\u201cFor years we\u2019ve lacked the tools and techniques to know who is going to survive a serious brain injury,\u201d said Owen.<\/p>\n

Solving the problem of recovery uncertainty<\/h3>\n

An interdisciplinary team of researchers from Western, in collaboration with neurologists at the London Health Sciences Centre and Lawson Health Research Institute, aimed to combat the uncertainty around brain injury recovery.<\/p>\n

The team was led by Loretta Norton, a psychology professor at King\u2019s University College at Western. Norton was one of the first researchers in the world to measure brain activity in the ICU.<\/p>\n

\"\"
\u00a9 shutterstock\/Pixel-Shot<\/figcaption><\/figure>\n

The researchers measured brain activity in 25 patients at one of London\u2019s two ICUs in the first few days after a serious brain injury. They then tested whether it could predict who would survive and who would not.<\/p>\n

\u201cWe previously found that information about the potential for recovery in these patients was captured in the way different brain regions communicate with each other,\u201d said Norton.<\/p>\n

\u201cIntact communication between brain regions is an important factor for regaining consciousness.\u201d<\/p>\n

Improving prediction accuracy with AI<\/h3>\n

The team\u2019s breakthrough occurred when they discovered that they could combine this imaging technique with an Artificial Intelligence<\/a> application known as Machine Learning.<\/p>\n

With this combination, they found that they could predict brain injury recovery with an accuracy of 80%. This is higher than the current standard of care.<\/p>\n

\u201cModern Artificial Intelligence has shown incredible predictive capabilities. Combining this with our existing imaging techniques was enough to better predict who will recover from their injuries,\u201d said Kolisnyk.<\/p>\n

More work must be done on the recovery prediction model<\/h3>\n

Although the results from the study are encouraging, the researchers say the prediction was not perfect and needs further research and testing.<\/p>\n

\u201cGiven that these models learn best when they have lots of data, we hope our findings will lead to further collaborations with ICUs across Canada,\u201d said Kazazian.<\/p>\n","protected":false},"excerpt":{"rendered":"

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